Tutorial goals:

The following tutorial is designed to give an overview of data analyses using single cell sequencing datasets using the Seurat procedure. Here, we address three main goals:

Download single cell raw count matrices

The original data is collected from this paper - Melms, J.C., Biermann, J., Huang, H. et al. A molecular single-cell lung atlas of lethal COVID-19. Nature 595, 114–119 (2021). https://doi.org/10.1038/s41586-021-03569-1

N.B.For this workshop, we have subsetted the control(C51ctr,C53ctr,C56ctr) & cases(L11cov, L22cov, L07cov) samples. Please download the smaller dataset and metadata from: https://github.com/gamazonlab/IGESWorkshop2023.git Once downloaded onto local computer, create a folder, move the files to the folder andunzip them.

# Load libraries
# If not already installed, please install first 

library(Seurat)
library(data.table)
library(dplyr)
library(patchwork)
library(cowplot)
library(patchwork)
library(multtest)
library(metap)
library(ggplot2)
library(enrichR)

Read in the count matrices for both cases and control samples

# change to data.frames and merge the two tables 
cases <- fread("cases.count.matrix.txt")
## Warning in fread("cases.count.matrix.txt"): Detected 15357 column names but the
## data has 15358 columns (i.e. invalid file). Added 1 extra default column name
## for the first column which is guessed to be row names or an index. Use
## setnames() afterwards if this guess is not correct, or fix the file write
## command that created the file to create a valid file.
cases <- as.data.frame(cases)
row.names(cases) <- cases$V1
cases <- cases[,-c(1)] 

controls <- fread("control.count.matrices.txt")
## Warning in fread("control.count.matrices.txt"): Detected 17308 column names but
## the data has 17309 columns (i.e. invalid file). Added 1 extra default column
## name for the first column which is guessed to be row names or an index. Use
## setnames() afterwards if this guess is not correct, or fix the file write
## command that created the file to create a valid file.
controls <- controls[,-c(1)]
controls <- as.data.frame(controls)
tot <- cbind(cases, controls)

dim(tot)
## [1] 34546 32665
tot[1:3, 1:3] 
##            TTCACGCAGCGAATGC-1_18 ATTATCCCACTGGACC-1_18 TTTACGTCACGACGTC-1_18
## AL627309.1                     0                     0                     0
## AL627309.5                     0                     0                     0
## AL627309.4                     0                     0                     0

Transform the count matrix to a Seurat object

# change to Seurat object
data <- CreateSeuratObject(counts = tot, project = "covid") # assign project name

# read in meta data
meta <- read.table("./meta/meta_data_subsetted.txt", h=T, sep="\t")

data <- AddMetaData(data, meta) # add the meta data to the data seurat object
data <- subset(data, subset = nFeature_RNA > 200) # remove low-quality cells with very few genes
data
## An object of class Seurat 
## 34546 features across 28575 samples within 1 assay 
## Active assay: RNA (34546 features, 0 variable features)

Add a mitochondrial QC metric to meta.data

data[["percent.mt"]] <- PercentageFeatureSet(data, pattern = "^MT-")

# Visualize QC metrics as a violin plot
VlnPlot(data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.

plot1 <- FeatureScatter(data, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(data, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2

### Quality control

Cell level and gene level

We filter cells that have unique feature counts over 2,500 or less than 200 We filter cells that have >5% mitochondrial count

data <- subset(data, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
data
## An object of class Seurat 
## 34546 features across 26697 samples within 1 assay 
## Active assay: RNA (34546 features, 0 variable features)

Data Normalization

Count depth scaling (counts per million CPM) normalization Log transformation

# normalization of feature expression measurement for each cell by total expression 
# then multiplied by scale factor (10k by default) and log-transformed

data <- NormalizeData(data, normalization.method = "LogNormalize", scale.factor = 10000)

Identifying highly variable features

data <- FindVariableFeatures(data, selection.method = "vst", nfeatures = 2000)

# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(data), 10)

# plot variable features with and without labels
plot1 <- VariableFeaturePlot(data)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
## When using repel, set xnudge and ynudge to 0 for optimal results
plot2
## Warning: Transformation introduced infinite values in continuous x-axis

Scaling the data:a standard pre-processing step prior to dimensional reduction techniques

all.genes <- rownames(data)
data <- ScaleData(data, features = all.genes)
## Centering and scaling data matrix

Linear dimension reduction

To reduce noise and facilitate visualization.

data <- RunPCA(data, features = VariableFeatures(object = data))
## PC_ 1 
## Positive:  DNAH12, DNAH9, LRRIQ1, CFAP43, SPAG17, DCDC1, CFAP299, HYDIN, AGBL4, DNAH6 
##     DNAH3, PACRG, ZBBX, RP1, ARMC3, PTPRT, ERICH3, CFAP47, DNAAF1, LMNTD1 
##     CFAP46, ADGB, VWA3B, DNAH7, ECT2L, TMEM232, CFAP157, VWA3A, TTC29, AL139815.1 
## Negative:  CTSB, PSAP, SLC8A1, KYNU, TFRC, PDE4B, CD163, KCNMA1, MSR1, SLC16A10 
##     FTL, SRGN, APOE, SLCO2B1, CTSD, SLC11A1, LGMN, ITGAX, MRC1, CTSZ 
##     FTH1, ATP1B3, HLA-DRA, ACSL1, CD74, B2M, GRN, RBPJ, GPNMB, SLC1A3 
## PC_ 2 
## Positive:  KYNU, CTSB, CTSS, ACSL1, PDE4B, KCNMA1, CD163, SLC16A10, MT-CO2, MSR1 
##     MT-CO1, CD74, SRGN, TFRC, SLCO2B1, MRC1, PSAP, CTSD, SLC11A1, FMN1 
##     ITGAX, HLA-DRA, THEMIS, LGMN, TOX, RNF144B, CTSZ, GK, TFEC, APOE 
## Negative:  COL5A2, COL6A3, CACNA1C, COL1A2, PRKG1, COL3A1, LAMA2, CALD1, CDH11, PDZRN3 
##     GPC6, LSAMP, COL5A1, RBMS3, BICC1, RYR2, DCN, CACNB2, FGF7, SLIT2 
##     THBS2, COL1A1, FN1, ROR2, DPYSL3, CCDC80, LUM, DLG2, CCDC102B, PDGFRA 
## PC_ 3 
## Positive:  SFTPB, SFTA3, ZNF385B, MECOM, ATP13A4, AL132857.1, ROS1, MAGI3, CADM1, PEBP4 
##     SHROOM3, LIMCH1, ABCA3, GPC5, LAMA3, NEDD4L, P3H2, RANBP17, GPRC5A, LMO7 
##     AC044810.2, LMO3, HOPX, AC027288.3, LINC01937, UNC13B, EMP2, AC112206.2, DAPK2, SNX25 
## Negative:  DNAH12, DNAH9, CFAP299, CFAP43, DNAH3, HYDIN, SPAG17, BICC1, ZBBX, DNAAF1 
##     ERICH3, ARMC3, CFAP46, ECT2L, LMNTD1, ADGB, CFAP47, NEK10, VWA3B, VWA3A 
##     CFAP54, PTPRT, COL1A2, CFAP157, COL3A1, DCDC1, COL5A2, AL139815.1, FGF14, TTC29 
## PC_ 4 
## Positive:  FTL, CTSD, PSAP, APOE, CTSB, FTH1, CTSZ, GRN, CTSL, GPNMB 
##     LGMN, HLA-DRA, CD63, TMSB4X, CCL18, GLUL, CSTB, CD68, ACSL1, IFI30 
##     C1QA, ACP5, CD74, S100A11, S100A6, C1QB, FMN1, SLC16A10, ACTB, CD81 
## Negative:  NCKAP5, GALNT18, AL355499.1, RTKN2, NRG3, THEMIS, TIMP3, ATF7IP2, LDB2, COL4A2 
##     GPM6A, MAP2, LINC01290, KHDRBS2, IFNG-AS1, AC010974.2, CAV1, AC022325.2, STXBP6, GLCCI1 
##     SYN3, NFATC2, PTPRB, CCDC85A, AC027288.3, CTNND2, SCEL, VWF, ADGRL2, CLIC5 
## PC_ 5 
## Positive:  ROS1, AC096531.2, AGBL1, ACOXL, LRRK2, ABCA3, SFTPC, ERBB4, AC010998.1, LHFPL3 
##     SCN1A, AFF3, CCDC141, LAMP3, SFTPA1, ARHGEF38, SLC22A3, TMEM163, AC092640.1, SFTPA2 
##     LHFPL3-AS2, TOX, SFTPB, ZNF385B, DMBT1, PTPRG, AC046195.1, LRP2, ALPL, RMST 
## Negative:  AL355499.1, NCKAP5, SCEL, RTKN2, AC027288.3, GPM6A, LINC01290, AC022325.2, EMP2, AC010974.2 
##     CTNND2, FAM189A2, LAMA3, ANKRD29, NCKAP5-AS2, AL359378.1, CAV1, CLIC5, GPRC5D-AS1, MYO16-AS1 
##     AC002066.1, GPRC5D, KHDRBS2, NRG1, ANOS1, MAP2, GALNT13, HULC, AC044810.2, COL4A3
DimPlot(data, reduction = "pca")

ElbowPlot(data)

# Cluster cells ## Groups similar cells based on their transcriptomics ## Using modularity optimization techniques such as the Louvain algorithm (default)

data <- FindNeighbors(data, dims = 1:10)
## Computing nearest neighbor graph
## Computing SNN
data <- FindClusters(data, resolution = 0.5)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 26697
## Number of edges: 868347
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9380
## Number of communities: 16
## Elapsed time: 2 seconds

Non-linear dimensional reduction (UMAP/tSNE)

##To learn the underlying manifold of the data in order to place similar cells together in low-dimensional space

# If you haven't installed UMAP, you can do so via reticulate::py_install(packages ='umap-learn')
data <- RunUMAP(data, dims = 1:10)
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 12:10:25 UMAP embedding parameters a = 0.9922 b = 1.112
## 12:10:25 Read 26697 rows and found 10 numeric columns
## 12:10:25 Using Annoy for neighbor search, n_neighbors = 30
## 12:10:25 Building Annoy index with metric = cosine, n_trees = 50
## 0%   10   20   30   40   50   60   70   80   90   100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 12:10:27 Writing NN index file to temp file /var/folders/0g/b0krx8616jvfdx7k8f7gcn580000gp/T//RtmppfHsqg/filecb8c11daae
## 12:10:27 Searching Annoy index using 1 thread, search_k = 3000
## 12:10:33 Annoy recall = 100%
## 12:10:33 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 12:10:34 Initializing from normalized Laplacian + noise (using irlba)
## 12:10:36 Commencing optimization for 200 epochs, with 1128696 positive edges
## 12:10:43 Optimization finished
# note that you can set `label = TRUE` or use the LabelClusters function to help label
# individual clusters
DimPlot(data, reduction = "umap")

# stopping point (save the clustered data)
#saveRDS(data, file = "./data_clustered.rds")

Post-clustering analysis

One approach after clustering is to perform differential expression analysis. There are two different differential expression analysis: - Between clusters - Between experimental conditions

Differential expression between clusters

Finding the specific genes that are differentially expressed between cell type clusters allows identification of markers. Seurat has inbuilt functions to find these gene markers, which can help in labeling of the clusters.

The function “FindMarkers” will help in identifying the differentially expressed genes in a cell type cluster.

# for example: find all markers of cluster 2
cluster2.markers <- FindMarkers(data, ident.1 = 2, min.pct = 0.25)
## For a more efficient implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the limma package
## --------------------------------------------
## install.packages('BiocManager')
## BiocManager::install('limma')
## --------------------------------------------
## After installation of limma, Seurat will automatically use the more 
## efficient implementation (no further action necessary).
## This message will be shown once per session
head(cluster2.markers, n = 5)
##         p_val avg_log2FC pct.1 pct.2 p_val_adj
## TMEM51      0   1.245006 0.270 0.055         0
## C1QA        0   1.280959 0.261 0.035         0
## THEMIS2     0   1.107038 0.250 0.035         0
## LAPTM5      0   1.088313 0.385 0.088         0
## PDE4B       0   1.961980 0.668 0.158         0

VlPlot and FeaturePlot can help visualize localization of marker genes with clusters

VlnPlot(data, features = c("TMEM51", "C1QA"))

FeaturePlot(data, features = c("TMEM51", "C1QA", "THEMIS2", "LAPTM5"))

Find Markers for all cell type clusters

# find markers for every cluster compared to all remaining cells, report only the positive
# ones
data.markers <- FindAllMarkers(data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
data.markers %>%
    group_by(cluster) %>%
    slice_max(n = 2, order_by = avg_log2FC)
## # A tibble: 32 × 7
## # Groups:   cluster [16]
##        p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene  
##        <dbl>      <dbl> <dbl> <dbl>     <dbl> <fct>   <chr> 
##  1 0               3.74 0.516 0.018 0         0       THEMIS
##  2 0               3.62 0.715 0.049 0         0       SKAP1 
##  3 0               6.14 0.881 0.021 0         1       COL3A1
##  4 0               5.84 0.755 0.011 0         1       COL1A1
##  5 0               3.14 0.772 0.089 0         2       KCNMA1
##  6 0               3.10 0.672 0.081 0         2       TFRC  
##  7 0               2.60 0.655 0.161 0         3       SLC8A1
##  8 1.45e-189       2.57 0.331 0.134 5.01e-185 3       VCAN  
##  9 0               5.14 0.719 0.032 0         4       ROBO2 
## 10 0               4.30 0.624 0.056 0         4       ELN   
## # ℹ 22 more rows

Assigning cell type identity to clusters

Once cell type clusters and marker genes are identified, we can assign labels to cell types. At this point, there is no inbuilt cell type labeling in Seurat, so this step is done manually. 💡 The database CellMarker has manually curated gene markers and cell annotations. Link: http://xteam.xbio.top/CellMarker/search.jsp?cellMarkerSpeciesType=Human&cellMarker=ELN

# For this workshop, the cell clusters have already been labeled
DimPlot(object = data, 
        reduction = "umap", 
        group.by = "cell_type_main")

Differential expression between conditions

In this case, the goal is to identify those genes that are differentially expressed between two experimental conditions such as (stimulated vs unstimulated cells) or between disease cases and healthy controls in specific cell type. However, having only 3 patients is probably too low, with many more patients it will work better to run pseudobulk analysis.

So another way to look broadly at these changes is to plot the average expression of both the covid and control cells and look for genes that are visual outliers on a scatter plot. Here, we take the average expression of both the covid and control naive T cells and B cell populations and generate the scatter plots, highlighting genes that exhibit dramatic responses to disease condition. To visualize the two disease conditions side-by-side, we can use the split.by argument to show each condition colored by cluster.

# plot this clustering
plot_grid(ncol = 2, DimPlot(data, label = T) + NoAxes(), DimPlot(data, group.by = "disease") + NoAxes())

First we subset our data for the desired cell cluster, then change the cell identities to the variable of comparison (which now in our case is the “disease”, e.g. Covid/Ctrl).❗️Since we are using the same expression data to do cell type marker identification and differential expression, this is prone to double dipping. If you want to learn more about how to resolve this issue, Link : https://doi.org/10.21203/rs.3.rs-3211191/v1

# select all cells in cluster 1
cell_selection <- subset(data, seurat_clusters == 2)
cell_selection <- SetIdent(cell_selection, value = "disease")
# Compute differential expression
DGE_cell_selection <- FindAllMarkers(cell_selection, log2FC.threshold = 0.2, test.use = "wilcox",
    min.pct = 0.1, min.diff.pct = 0.2, only.pos = TRUE, max.cells.per.ident = 50,
    assay = "RNA")
## Calculating cluster COVID-19
## Calculating cluster normal

❗️Imbalanced sex in the subjects can introduce bias during DEG analysis. Make sure to remove if data is available for sex chromosome related genes.

DGE_cell_selection %>%
    group_by(cluster) %>%
    top_n(-5, p_val) -> top5_cell_selection

VlnPlot(cell_selection, features = as.character(unique(top5_cell_selection$gene)),
    ncol = 5, group.by = "disease", assay = "RNA", pt.size = 0.1)

Batch effects

When testing DEG across conditions, sample differences can create a bias. So first, let's check how the top DGEs are expressed across the individuals:

VlnPlot(cell_selection, group.by = "rds.biosample_id", features = as.character(unique(top5_cell_selection$gene)),
    ncol = 5, assay = "RNA", pt.size = 0)

❗️It happens here, that many of the genes are evenly expressed across samples. In the case that DEG results are dominated by a single sample, one approach is to use the "downsample" Seurat function to make sure that every sample has the same number of cells.

Plot as dotplot, for whole the dataset:

# Define as Covid or Ctrl in the df and add a gene column
DGE_cell_selection$direction = ifelse(DGE_cell_selection$avg_log2FC > 0, "Covid",
    "Ctrl")
DGE_cell_selection$gene = rownames(DGE_cell_selection)

DGE_cell_selection %>%
    group_by(direction) %>%
    top_n(-20, p_val) -> top20_cell_selection
DotPlot(cell_selection, features = rev(as.character(unique(top20_cell_selection$gene))),
    group.by = "rds.biosample_id", assay = "RNA") + coord_flip()

Gene Set Analysis

If interested in checking the function of the differentially expressed genes identified above, you can use hypergeometric enrichment test

# Check available databases to perform enrichment (then choose one)
enrichR::listEnrichrDbs()
##     geneCoverage genesPerTerm
## 1          13362          275
## 2          27884         1284
## 3           6002           77
## 4          47172         1370
## 5          47107          509
## 6          21493         3713
## 7           1295           18
## 8           3185           73
## 9           2854           34
## 10         15057          300
## 11          4128           48
## 12         34061          641
## 13          7504          155
## 14         16399          247
## 15         12753           57
## 16         23726          127
## 17         32740           85
## 18         13373          258
## 19         19270          388
## 20         13236           82
## 21         14264           58
## 22          3096           31
## 23         22288         4368
## 24          4533           37
## 25         10231          158
## 26          2741            5
## 27          5655          342
## 28         10406          715
## 29         10493          200
## 30         11251          100
## 31          8695          100
## 32          1759           25
## 33          2178           89
## 34           851           15
## 35         10061          106
## 36         11250          166
## 37         15406          300
## 38         17711          300
## 39         17576          300
## 40         15797          176
## 41         12232          343
## 42         13572          301
## 43          6454          301
## 44          3723           47
## 45          7588           35
## 46          7682           78
## 47          7324          172
## 48          8469          122
## 49         13121          305
## 50         26382         1811
## 51         29065         2123
## 52           280            9
## 53         13877          304
## 54         15852          912
## 55          4320          129
## 56          4271          128
## 57         10496          201
## 58          1678           21
## 59           756           12
## 60          3800           48
## 61          2541           39
## 62          1918           39
## 63          5863           51
## 64          6768           47
## 65         25651          807
## 66         19129         1594
## 67         23939          293
## 68         23561          307
## 69         23877          302
## 70         15886            9
## 71         24350          299
## 72          3102           25
## 73         31132          298
## 74         30832          302
## 75         48230         1429
## 76          5613           36
## 77          9559           73
## 78          9448           63
## 79         16725         1443
## 80         19249         1443
## 81         15090          282
## 82         16129          292
## 83         15309          308
## 84         15103          318
## 85         15022          290
## 86         15676          310
## 87         15854          279
## 88         15015          321
## 89          3788          159
## 90          3357          153
## 91         12668          300
## 92         12638          300
## 93          8973           64
## 94          7010           87
## 95          5966           51
## 96         15562          887
## 97         17850          300
## 98         17660          300
## 99          1348           19
## 100          934           13
## 101         2541           39
## 102         2041           42
## 103         5209          300
## 104        49238         1550
## 105         2243           19
## 106        19586          545
## 107        22440          505
## 108         8184           24
## 109        18329          161
## 110        15755           28
## 111        10271           22
## 112        10427           38
## 113        10601           25
## 114        13822           21
## 115         8002          143
## 116        10089           45
## 117        13247           49
## 118        21809         2316
## 119        23601         2395
## 120        20883          299
## 121        19612          299
## 122        25983          299
## 123        19500          137
## 124        14893          128
## 125        17598         1208
## 126         5902          109
## 127        12486          299
## 128         1073          100
## 129        19513          117
## 130        14433           36
## 131         8655           61
## 132        11459           39
## 133        19741          270
## 134        27360          802
## 135        13072           26
## 136        13464           45
## 137        13787          200
## 138        13929          200
## 139        16964          200
## 140        17258          200
## 141        10352           58
## 142        10471           76
## 143        12419          491
## 144        19378           37
## 145         6201           45
## 146         4558           54
## 147         3264           22
## 148         7802           92
## 149         8551           98
## 150        12444           23
## 151         9000           20
## 152         7744          363
## 153         6204          387
## 154        13420           32
## 155        14148          122
## 156         9813           49
## 157         1397           13
## 158         9116           22
## 159        17464           63
## 160          394           73
## 161        11851          586
## 162         8189          421
## 163        18704          100
## 164         5605           39
## 165         5718           31
## 166        14156           40
## 167        16979          295
## 168         4383          146
## 169        54974          483
## 170        12118          448
## 171        12361          124
## 172         9763          139
## 173         8078          102
## 174         7173           43
## 175         5833          100
## 176        14937           33
## 177        11497           80
## 178        11936           34
## 179         9767           33
## 180        14167           80
## 181        17851          102
## 182        16853          360
## 183         6654          136
## 184         1683           10
## 185        20414          112
## 186        26076          250
## 187        26338          250
## 188        25381          250
## 189        25409          250
## 190        11980          250
## 191        31158          805
## 192        30006          815
## 193        13370          103
## 194        13697          343
## 195         2183           18
## 196        12765           13
## 197         1509          100
## 198        18365         1214
## 199        13525          175
## 200         9525          245
## 201         9440          245
## 202         3857           80
## 203        10489           61
## 204         1198           23
## 205         1882           47
## 206         1552           16
## 207         6713           68
## 208          936           15
## 209         8220          146
## 210         9021          793
## 211         8076           96
## 212        14698           33
## 213        10972           85
## 214        12126           38
## 215        13662           12
## 216        18290           34
## 217        12081           50
## 218        12853          485
## 219         3712            9
## 220        19178          218
## 221        19434          369
## 222        19379          250
## 223        10428          115
## 224         8044           42
##                                            libraryName
## 1                                  Genome_Browser_PWMs
## 2                             TRANSFAC_and_JASPAR_PWMs
## 3                            Transcription_Factor_PPIs
## 4                                            ChEA_2013
## 5                     Drug_Perturbations_from_GEO_2014
## 6                              ENCODE_TF_ChIP-seq_2014
## 7                                        BioCarta_2013
## 8                                        Reactome_2013
## 9                                    WikiPathways_2013
## 10                 Disease_Signatures_from_GEO_up_2014
## 11                                           KEGG_2013
## 12                          TF-LOF_Expression_from_GEO
## 13                                 TargetScan_microRNA
## 14                                    PPI_Hub_Proteins
## 15                          GO_Molecular_Function_2015
## 16                                           GeneSigDB
## 17                                 Chromosome_Location
## 18                                    Human_Gene_Atlas
## 19                                    Mouse_Gene_Atlas
## 20                          GO_Cellular_Component_2015
## 21                          GO_Biological_Process_2015
## 22                            Human_Phenotype_Ontology
## 23                     Epigenomics_Roadmap_HM_ChIP-seq
## 24                                            KEA_2013
## 25                   NURSA_Human_Endogenous_Complexome
## 26                                               CORUM
## 27                             SILAC_Phosphoproteomics
## 28                     MGI_Mammalian_Phenotype_Level_3
## 29                     MGI_Mammalian_Phenotype_Level_4
## 30                                         Old_CMAP_up
## 31                                       Old_CMAP_down
## 32                                        OMIM_Disease
## 33                                       OMIM_Expanded
## 34                                           VirusMINT
## 35                                MSigDB_Computational
## 36                         MSigDB_Oncogenic_Signatures
## 37               Disease_Signatures_from_GEO_down_2014
## 38                     Virus_Perturbations_from_GEO_up
## 39                   Virus_Perturbations_from_GEO_down
## 40                       Cancer_Cell_Line_Encyclopedia
## 41                            NCI-60_Cancer_Cell_Lines
## 42         Tissue_Protein_Expression_from_ProteomicsDB
## 43   Tissue_Protein_Expression_from_Human_Proteome_Map
## 44                                    HMDB_Metabolites
## 45                               Pfam_InterPro_Domains
## 46                          GO_Biological_Process_2013
## 47                          GO_Cellular_Component_2013
## 48                          GO_Molecular_Function_2013
## 49                                Allen_Brain_Atlas_up
## 50                             ENCODE_TF_ChIP-seq_2015
## 51                   ENCODE_Histone_Modifications_2015
## 52                   Phosphatase_Substrates_from_DEPOD
## 53                              Allen_Brain_Atlas_down
## 54                   ENCODE_Histone_Modifications_2013
## 55                           Achilles_fitness_increase
## 56                           Achilles_fitness_decrease
## 57                        MGI_Mammalian_Phenotype_2013
## 58                                       BioCarta_2015
## 59                                       HumanCyc_2015
## 60                                           KEGG_2015
## 61                                     NCI-Nature_2015
## 62                                        Panther_2015
## 63                                   WikiPathways_2015
## 64                                       Reactome_2015
## 65                                              ESCAPE
## 66                                          HomoloGene
## 67                 Disease_Perturbations_from_GEO_down
## 68                   Disease_Perturbations_from_GEO_up
## 69                    Drug_Perturbations_from_GEO_down
## 70                    Genes_Associated_with_NIH_Grants
## 71                      Drug_Perturbations_from_GEO_up
## 72                                            KEA_2015
## 73                      Gene_Perturbations_from_GEO_up
## 74                    Gene_Perturbations_from_GEO_down
## 75                                           ChEA_2015
## 76                                               dbGaP
## 77                            LINCS_L1000_Chem_Pert_up
## 78                          LINCS_L1000_Chem_Pert_down
## 79                         GTEx_Tissue_Expression_Down
## 80                           GTEx_Tissue_Expression_Up
## 81                  Ligand_Perturbations_from_GEO_down
## 82                   Aging_Perturbations_from_GEO_down
## 83                     Aging_Perturbations_from_GEO_up
## 84                    Ligand_Perturbations_from_GEO_up
## 85                    MCF7_Perturbations_from_GEO_down
## 86                      MCF7_Perturbations_from_GEO_up
## 87                 Microbe_Perturbations_from_GEO_down
## 88                   Microbe_Perturbations_from_GEO_up
## 89               LINCS_L1000_Ligand_Perturbations_down
## 90                 LINCS_L1000_Ligand_Perturbations_up
## 91            L1000_Kinase_and_GPCR_Perturbations_down
## 92              L1000_Kinase_and_GPCR_Perturbations_up
## 93                                       Reactome_2016
## 94                                           KEGG_2016
## 95                                   WikiPathways_2016
## 96           ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X
## 97                  Kinase_Perturbations_from_GEO_down
## 98                    Kinase_Perturbations_from_GEO_up
## 99                                       BioCarta_2016
## 100                                      HumanCyc_2016
## 101                                    NCI-Nature_2016
## 102                                       Panther_2016
## 103                                         DrugMatrix
## 104                                          ChEA_2016
## 105                                              huMAP
## 106                                     Jensen_TISSUES
## 107  RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO
## 108                       MGI_Mammalian_Phenotype_2017
## 109                                Jensen_COMPARTMENTS
## 110                                    Jensen_DISEASES
## 111                                       BioPlex_2017
## 112                         GO_Cellular_Component_2017
## 113                         GO_Molecular_Function_2017
## 114                         GO_Biological_Process_2017
## 115                        GO_Cellular_Component_2017b
## 116                        GO_Molecular_Function_2017b
## 117                        GO_Biological_Process_2017b
## 118                                     ARCHS4_Tissues
## 119                                  ARCHS4_Cell-lines
## 120                                   ARCHS4_IDG_Coexp
## 121                               ARCHS4_Kinases_Coexp
## 122                                   ARCHS4_TFs_Coexp
## 123                            SysMyo_Muscle_Gene_Sets
## 124                                    miRTarBase_2017
## 125                           TargetScan_microRNA_2017
## 126               Enrichr_Libraries_Most_Popular_Genes
## 127            Enrichr_Submissions_TF-Gene_Coocurrence
## 128         Data_Acquisition_Method_Most_Popular_Genes
## 129                                             DSigDB
## 130                         GO_Biological_Process_2018
## 131                         GO_Cellular_Component_2018
## 132                         GO_Molecular_Function_2018
## 133            TF_Perturbations_Followed_by_Expression
## 134                           Chromosome_Location_hg19
## 135                  NIH_Funded_PIs_2017_Human_GeneRIF
## 136                  NIH_Funded_PIs_2017_Human_AutoRIF
## 137           Rare_Diseases_AutoRIF_ARCHS4_Predictions
## 138           Rare_Diseases_GeneRIF_ARCHS4_Predictions
## 139     NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions
## 140     NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions
## 141                   Rare_Diseases_GeneRIF_Gene_Lists
## 142                   Rare_Diseases_AutoRIF_Gene_Lists
## 143                                    SubCell_BarCode
## 144                                  GWAS_Catalog_2019
## 145                            WikiPathways_2019_Human
## 146                            WikiPathways_2019_Mouse
## 147                  TRRUST_Transcription_Factors_2019
## 148                                    KEGG_2019_Human
## 149                                    KEGG_2019_Mouse
## 150                              InterPro_Domains_2019
## 151                                  Pfam_Domains_2019
## 152      DepMap_WG_CRISPR_Screens_Broad_CellLines_2019
## 153     DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019
## 154               MGI_Mammalian_Phenotype_Level_4_2019
## 155                                 UK_Biobank_GWAS_v1
## 156                                     BioPlanet_2019
## 157                                       ClinVar_2019
## 158                                        PheWeb_2019
## 159                                           DisGeNET
## 160                               HMS_LINCS_KinomeScan
## 161                               CCLE_Proteomics_2020
## 162                                  ProteomicsDB_2020
## 163                        lncHUB_lncRNA_Co-Expression
## 164                      Virus-Host_PPI_P-HIPSTer_2020
## 165                        Elsevier_Pathway_Collection
## 166                     Table_Mining_of_CRISPR_Studies
## 167                         COVID-19_Related_Gene_Sets
## 168                               MSigDB_Hallmark_2020
## 169               Enrichr_Users_Contributed_Lists_2020
## 170                                      TG_GATES_2020
## 171                   Allen_Brain_Atlas_10x_scRNA_2021
## 172               Descartes_Cell_Types_and_Tissue_2021
## 173                                    KEGG_2021_Human
## 174                             WikiPathway_2021_Human
## 175 HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression
## 176                         GO_Biological_Process_2021
## 177                         GO_Cellular_Component_2021
## 178                         GO_Molecular_Function_2021
## 179               MGI_Mammalian_Phenotype_Level_4_2021
## 180                          CellMarker_Augmented_2021
## 181                            Orphanet_Augmented_2021
## 182                    COVID-19_Related_Gene_Sets_2021
## 183                           PanglaoDB_Augmented_2021
## 184                            Azimuth_Cell_Types_2021
## 185                          PhenGenI_Association_2021
## 186         RNAseq_Automatic_GEO_Signatures_Human_Down
## 187           RNAseq_Automatic_GEO_Signatures_Human_Up
## 188         RNAseq_Automatic_GEO_Signatures_Mouse_Down
## 189           RNAseq_Automatic_GEO_Signatures_Mouse_Up
## 190                         GTEx_Aging_Signatures_2021
## 191                                 HDSigDB_Human_2021
## 192                                 HDSigDB_Mouse_2021
## 193                    HuBMAP_ASCTplusB_augmented_2022
## 194                             FANTOM6_lncRNA_KD_DEGs
## 195                           MAGMA_Drugs_and_Diseases
## 196                                     PFOCR_Pathways
## 197                                     Tabula_Sapiens
## 198                                          ChEA_2022
## 199                    Diabetes_Perturbations_GEO_2022
## 200               LINCS_L1000_Chem_Pert_Consensus_Sigs
## 201               LINCS_L1000_CRISPR_KO_Consensus_Sigs
## 202                                       Tabula_Muris
## 203                                      Reactome_2022
## 204                                         SynGO_2022
## 205                  GlyGen_Glycosylated_Proteins_2022
## 206                              IDG_Drug_Targets_2022
## 207                        KOMP2_Mouse_Phenotypes_2022
## 208            Metabolomics_Workbench_Metabolites_2022
## 209                         Proteomics_Drug_Atlas_2023
## 210                            The_Kinase_Library_2023
## 211                               GTEx_Tissues_V8_2023
## 212                         GO_Biological_Process_2023
## 213                         GO_Cellular_Component_2023
## 214                         GO_Molecular_Function_2023
## 215                                PFOCR_Pathways_2023
## 216                                  GWAS_Catalog_2023
## 217                                      GeDiPNet_2023
## 218                                        MAGNET_2023
## 219                                       Azimuth_2023
## 220                                  Rummagene_kinases
## 221                               Rummagene_signatures
## 222                    Rummagene_transcription_factors
## 223                                       MoTrPAC_2023
## 224                             WikiPathway_2023_Human
##                                                                                link
## 1                          http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/
## 2                                          http://jaspar.genereg.net/html/DOWNLOAD/
## 3                                                                                  
## 4                                    http://amp.pharm.mssm.edu/lib/cheadownload.jsp
## 5                                                  http://www.ncbi.nlm.nih.gov/geo/
## 6                                      http://genome.ucsc.edu/ENCODE/downloads.html
## 7                               https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
## 8                                       http://www.reactome.org/download/index.html
## 9                           http://www.wikipathways.org/index.php/Download_Pathways
## 10                                                 http://www.ncbi.nlm.nih.gov/geo/
## 11                                                http://www.kegg.jp/kegg/download/
## 12                                                 http://www.ncbi.nlm.nih.gov/geo/
## 13        http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61
## 14                                                    http://amp.pharm.mssm.edu/X2K
## 15                       http://www.geneontology.org/GO.downloads.annotations.shtml
## 16                                        https://pubmed.ncbi.nlm.nih.gov/22110038/
## 17                         http://software.broadinstitute.org/gsea/msigdb/index.jsp
## 18                                                     http://biogps.org/downloads/
## 19                                                     http://biogps.org/downloads/
## 20                       http://www.geneontology.org/GO.downloads.annotations.shtml
## 21                       http://www.geneontology.org/GO.downloads.annotations.shtml
## 22                                         http://www.human-phenotype-ontology.org/
## 23                                               http://www.roadmapepigenomics.org/
## 24                                 http://amp.pharm.mssm.edu/lib/keacommandline.jsp
## 25                                            https://www.nursa.org/nursa/index.jsf
## 26                              http://mips.helmholtz-muenchen.de/genre/proj/corum/
## 27                                 http://amp.pharm.mssm.edu/lib/keacommandline.jsp
## 28                                                  http://www.informatics.jax.org/
## 29                                                  http://www.informatics.jax.org/
## 30                                              http://www.broadinstitute.org/cmap/
## 31                                              http://www.broadinstitute.org/cmap/
## 32                                                    http://www.omim.org/downloads
## 33                                                    http://www.omim.org/downloads
## 34                                        http://mint.bio.uniroma2.it/download.html
## 35                        http://www.broadinstitute.org/gsea/msigdb/collections.jsp
## 36                        http://www.broadinstitute.org/gsea/msigdb/collections.jsp
## 37                                                 http://www.ncbi.nlm.nih.gov/geo/
## 38                                                 http://www.ncbi.nlm.nih.gov/geo/
## 39                                                 http://www.ncbi.nlm.nih.gov/geo/
## 40                                   https://portals.broadinstitute.org/ccle/home\n
## 41                                                     http://biogps.org/downloads/
## 42                                                    https://www.proteomicsdb.org/
## 43                                        http://www.humanproteomemap.org/index.php
## 44                                                     http://www.hmdb.ca/downloads
## 45                                      ftp://ftp.ebi.ac.uk/pub/databases/interpro/
## 46                       http://www.geneontology.org/GO.downloads.annotations.shtml
## 47                       http://www.geneontology.org/GO.downloads.annotations.shtml
## 48                       http://www.geneontology.org/GO.downloads.annotations.shtml
## 49                                                        http://www.brain-map.org/
## 50                                     http://genome.ucsc.edu/ENCODE/downloads.html
## 51                                     http://genome.ucsc.edu/ENCODE/downloads.html
## 52                                                  http://www.koehn.embl.de/depod/
## 53                                                        http://www.brain-map.org/
## 54                                     http://genome.ucsc.edu/ENCODE/downloads.html
## 55                                           http://www.broadinstitute.org/achilles
## 56                                           http://www.broadinstitute.org/achilles
## 57                                                  http://www.informatics.jax.org/
## 58                              https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
## 59                                                             http://humancyc.org/
## 60                                                http://www.kegg.jp/kegg/download/
## 61                                                          http://pid.nci.nih.gov/
## 62                                                        http://www.pantherdb.org/
## 63                          http://www.wikipathways.org/index.php/Download_Pathways
## 64                                      http://www.reactome.org/download/index.html
## 65                                                 http://www.maayanlab.net/ESCAPE/
## 66                                           http://www.ncbi.nlm.nih.gov/homologene
## 67                                                 http://www.ncbi.nlm.nih.gov/geo/
## 68                                                 http://www.ncbi.nlm.nih.gov/geo/
## 69                                                 http://www.ncbi.nlm.nih.gov/geo/
## 70                                          https://grants.nih.gov/grants/oer.htm\n
## 71                                                 http://www.ncbi.nlm.nih.gov/geo/
## 72                                                http://amp.pharm.mssm.edu/Enrichr
## 73                                                 http://www.ncbi.nlm.nih.gov/geo/
## 74                                                 http://www.ncbi.nlm.nih.gov/geo/
## 75                                                http://amp.pharm.mssm.edu/Enrichr
## 76                                                  http://www.ncbi.nlm.nih.gov/gap
## 77                                                                 https://clue.io/
## 78                                                                 https://clue.io/
## 79                                                       http://www.gtexportal.org/
## 80                                                       http://www.gtexportal.org/
## 81                                                 http://www.ncbi.nlm.nih.gov/geo/
## 82                                                 http://www.ncbi.nlm.nih.gov/geo/
## 83                                                 http://www.ncbi.nlm.nih.gov/geo/
## 84                                                 http://www.ncbi.nlm.nih.gov/geo/
## 85                                                 http://www.ncbi.nlm.nih.gov/geo/
## 86                                                 http://www.ncbi.nlm.nih.gov/geo/
## 87                                                 http://www.ncbi.nlm.nih.gov/geo/
## 88                                                 http://www.ncbi.nlm.nih.gov/geo/
## 89                                                                 https://clue.io/
## 90                                                                 https://clue.io/
## 91                                                                 https://clue.io/
## 92                                                                 https://clue.io/
## 93                                      http://www.reactome.org/download/index.html
## 94                                                http://www.kegg.jp/kegg/download/
## 95                          http://www.wikipathways.org/index.php/Download_Pathways
## 96                                                                                 
## 97                                                 http://www.ncbi.nlm.nih.gov/geo/
## 98                                                 http://www.ncbi.nlm.nih.gov/geo/
## 99                               http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
## 100                                                            http://humancyc.org/
## 101                                                         http://pid.nci.nih.gov/
## 102                                               http://www.pantherdb.org/pathway/
## 103                                           https://ntp.niehs.nih.gov/drugmatrix/
## 104                                               http://amp.pharm.mssm.edu/Enrichr
## 105                                                    http://proteincomplexes.org/
## 106                                                   http://tissues.jensenlab.org/
## 107                                                http://www.ncbi.nlm.nih.gov/geo/
## 108                                                 http://www.informatics.jax.org/
## 109                                              http://compartments.jensenlab.org/
## 110                                                  http://diseases.jensenlab.org/
## 111                                                 http://bioplex.hms.harvard.edu/
## 112                                                    http://www.geneontology.org/
## 113                                                    http://www.geneontology.org/
## 114                                                    http://www.geneontology.org/
## 115                                                    http://www.geneontology.org/
## 116                                                    http://www.geneontology.org/
## 117                                                    http://www.geneontology.org/
## 118                                                http://amp.pharm.mssm.edu/archs4
## 119                                                http://amp.pharm.mssm.edu/archs4
## 120                                                http://amp.pharm.mssm.edu/archs4
## 121                                                http://amp.pharm.mssm.edu/archs4
## 122                                                http://amp.pharm.mssm.edu/archs4
## 123                                                     http://sys-myo.rhcloud.com/
## 124                                              http://mirtarbase.mbc.nctu.edu.tw/
## 125                                                      http://www.targetscan.org/
## 126                                               http://amp.pharm.mssm.edu/Enrichr
## 127                                               http://amp.pharm.mssm.edu/Enrichr
## 128                                               http://amp.pharm.mssm.edu/Enrichr
## 129                                   http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/
## 130                                                    http://www.geneontology.org/
## 131                                                    http://www.geneontology.org/
## 132                                                    http://www.geneontology.org/
## 133                                                http://www.ncbi.nlm.nih.gov/geo/
## 134                                   http://hgdownload.cse.ucsc.edu/downloads.html
## 135                                            https://www.ncbi.nlm.nih.gov/pubmed/
## 136                                            https://www.ncbi.nlm.nih.gov/pubmed/
## 137                                            https://amp.pharm.mssm.edu/geneshot/
## 138                                 https://www.ncbi.nlm.nih.gov/gene/about-generif
## 139                                            https://www.ncbi.nlm.nih.gov/pubmed/
## 140                                            https://www.ncbi.nlm.nih.gov/pubmed/
## 141                                 https://www.ncbi.nlm.nih.gov/gene/about-generif
## 142                                            https://amp.pharm.mssm.edu/geneshot/
## 143                                                  http://www.subcellbarcode.org/
## 144                                                      https://www.ebi.ac.uk/gwas
## 145                                                   https://www.wikipathways.org/
## 146                                                   https://www.wikipathways.org/
## 147                                                https://www.grnpedia.org/trrust/
## 148                                                            https://www.kegg.jp/
## 149                                                            https://www.kegg.jp/
## 150                                                 https://www.ebi.ac.uk/interpro/
## 151                                                          https://pfam.xfam.org/
## 152                                                             https://depmap.org/
## 153                                                             https://depmap.org/
## 154                                                 http://www.informatics.jax.org/
## 155                                           https://www.ukbiobank.ac.uk/tag/gwas/
## 156                                               https://tripod.nih.gov/bioplanet/
## 157                                           https://www.ncbi.nlm.nih.gov/clinvar/
## 158                                                    http://pheweb.sph.umich.edu/
## 159                                                        https://www.disgenet.org
## 160                                        http://lincs.hms.harvard.edu/kinomescan/
## 161                                         https://portals.broadinstitute.org/ccle
## 162                                                   https://www.proteomicsdb.org/
## 163                                              https://amp.pharm.mssm.edu/lnchub/
## 164                                                            http://phipster.org/
## 165                                       http://www.transgene.ru/disease-pathways/
## 166                                                                                
## 167                                              https://amp.pharm.mssm.edu/covid19
## 168                        https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp 
## 169                                                 https://maayanlab.cloud/Enrichr
## 170                                           https://toxico.nibiohn.go.jp/english/
## 171                                                   https://portal.brain-map.org/
## 172 https://descartes.brotmanbaty.org/bbi/human-gene-expression-during-development/
## 173                                                            https://www.kegg.jp/
## 174                                                   https://www.wikipathways.org/
## 175                           https://hubmapconsortium.github.io/ccf-asct-reporter/
## 176                                                    http://www.geneontology.org/
## 177                                                    http://www.geneontology.org/
## 178                                                    http://www.geneontology.org/
## 179                                                 http://www.informatics.jax.org/
## 180                                           http://biocc.hrbmu.edu.cn/CellMarker/
## 181                                                       http://www.orphadata.org/
## 182                                                https://maayanlab.cloud/covid19/
## 183                                                           https://panglaodb.se/
## 184                                           https://azimuth.hubmapconsortium.org/
## 185                                        https://www.ncbi.nlm.nih.gov/gap/phegeni
## 186                                                 https://maayanlab.cloud/archs4/
## 187                                                 https://maayanlab.cloud/archs4/
## 188                                                 https://maayanlab.cloud/archs4/
## 189                                                 https://maayanlab.cloud/archs4/
## 190                                                         https://gtexportal.org/
## 191                                                         https://www.hdinhd.org/
## 192                                                         https://www.hdinhd.org/
## 193                           https://hubmapconsortium.github.io/ccf-asct-reporter/
## 194                                                  https://fantom.gsc.riken.jp/6/
## 195                      https://github.com/nybell/drugsets/tree/main/DATA/GENESETS
## 196                                                 https://pfocr.wikipathways.org/
## 197                                  https://tabula-sapiens-portal.ds.czbiohub.org/
## 198                                                  https://maayanlab.cloud/chea3/
## 199               https://appyters.maayanlab.cloud/#/Gene_Expression_T2D_Signatures
## 200                                 https://maayanlab.cloud/sigcom-lincs/#/Download
## 201                                 https://maayanlab.cloud/sigcom-lincs/#/Download
## 202                                           https://tabula-muris.ds.czbiohub.org/
## 203                                              https://reactome.org/download-data
## 204                                                    https://www.syngoportal.org/
## 205                                                         https://www.glygen.org/
## 206                                                        https://drugcentral.org/
## 207                                                 https://www.mousephenotype.org/
## 208                                          https://www.metabolomicsworkbench.org/
## 209                              https://www.nature.com/articles/s41587-022-01539-0
## 210                                     https://kinase-library.phosphosite.org/site
## 211                                                    https://gtexportal.org/home/
## 212                                                    http://www.geneontology.org/
## 213                                                    http://www.geneontology.org/
## 214                                                    http://www.geneontology.org/
## 215                                                 https://pfocr.wikipathways.org/
## 216                                                      https://www.ebi.ac.uk/gwas
## 217                                                http://gedipnet.bicnirrh.res.in/
## 218                                         https://magnet-winterlab.herokuapp.com/
## 219                                           https://azimuth.hubmapconsortium.org/
## 220                                                          https://rummagene.com/
## 221                                                          https://rummagene.com/
## 222                                                          https://rummagene.com/
## 223                                                       https://motrpac-data.org/
## 224                                                   https://www.wikipathways.org/
##     numTerms                                  appyter categoryId
## 1        615 ea115789fcbf12797fd692cec6df0ab4dbc79c6a          1
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## 123     1135                                                   8
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## 172      172                                                   5
## 173      320                                                   2
## 174      622                                                   7
## 175      344                                                   5
## 176     6036                                                   7
## 177      511                                                   7
## 178     1274                                                   7
## 179     4601                                                   3
## 180     1097                                                   5
## 181     3774                                                   4
## 182      478                                                   4
## 183      178                                                   5
## 184      341                                                   5
## 185      950                                                   4
## 186     4269                                                   8
## 187     4269                                                   8
## 188     4216                                                   8
## 189     4216                                                   8
## 190      270                                                   4
## 191     2564                                                   4
## 192     2579                                                   4
## 193      777                                                   5
## 194      350                                                   1
## 195     1395                                                   4
## 196    17326                                                   7
## 197      469                                                   5
## 198      757                                                   1
## 199      601                                                   4
## 200    10850                                                   4
## 201    10424                                                   4
## 202      106                                                   5
## 203     1818                                                   2
## 204      118                                                   3
## 205      338                                                   2
## 206      888                                                   4
## 207      529                                                   3
## 208      233                                                   2
## 209     1748                                                   4
## 210      303                                                   2
## 211      511                                                   5
## 212     5407                                                   3
## 213      474                                                   3
## 214     1147                                                   3
## 215    21845                                                   2
## 216     5271                                                   4
## 217     2388                                                   4
## 218       72                                                   5
## 219     1425                                                   5
## 220     3810                                                   2
## 221    11130                                                   8
## 222     7244                                                   1
## 223      225                                                   5
## 224      801                                                   2
# Perform enrichment
enrich_results <- enrichr(genes = DGE_cell_selection$gene[DGE_cell_selection$avg_log2FC >
    0], databases = "GO_Biological_Process_2017b")[[1]]
## Uploading data to Enrichr... Done.
##   Querying GO_Biological_Process_2017b... Done.
## Parsing results... Done.
# Visualize your results using a simple barplot
par(mfrow = c(1, 1), mar = c(3, 25, 2, 1))
barplot(height = -log10(enrich_results$P.value)[10:1], names.arg = enrich_results$Term[10:1],
    horiz = TRUE, las = 1, border = FALSE, cex.names = 0.6)
abline(v = c(-log10(0.05)), lty = 2)
abline(v = 0, lty = 1)

Session Info

sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/Chicago
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] enrichR_3.2         ggplot2_3.4.3       metap_1.8          
##  [4] multtest_2.56.0     Biobase_2.60.0      BiocGenerics_0.46.0
##  [7] cowplot_1.1.1       patchwork_1.1.3     dplyr_1.1.3        
## [10] data.table_1.14.8   SeuratObject_4.1.4  Seurat_4.4.0       
## 
## loaded via a namespace (and not attached):
##   [1] mathjaxr_1.6-0         RColorBrewer_1.1-3     rstudioapi_0.15.0     
##   [4] jsonlite_1.8.7         magrittr_2.0.3         TH.data_1.1-2         
##   [7] spatstat.utils_3.0-3   farver_2.1.1           rmarkdown_2.25        
##  [10] vctrs_0.6.3            ROCR_1.0-11            spatstat.explore_3.2-3
##  [13] htmltools_0.5.6        curl_5.0.2             plotrix_3.8-2         
##  [16] sass_0.4.7             sctransform_0.4.0      parallelly_1.36.0     
##  [19] KernSmooth_2.23-22     bslib_0.5.1            htmlwidgets_1.6.2     
##  [22] ica_1.0-3              sandwich_3.0-2         plyr_1.8.8            
##  [25] plotly_4.10.2          zoo_1.8-12             cachem_1.0.8          
##  [28] igraph_1.5.1           mime_0.12              lifecycle_1.0.3       
##  [31] pkgconfig_2.0.3        Matrix_1.6-1.1         R6_2.5.1              
##  [34] fastmap_1.1.1          rbibutils_2.2.15       fitdistrplus_1.1-11   
##  [37] future_1.33.0          shiny_1.7.5            numDeriv_2016.8-1.1   
##  [40] digest_0.6.33          colorspace_2.1-0       tensor_1.5            
##  [43] irlba_2.3.5.1          labeling_0.4.3         WriteXLS_6.4.0        
##  [46] progressr_0.14.0       fansi_1.0.4            spatstat.sparse_3.0-2 
##  [49] httr_1.4.7             TFisher_0.2.0          polyclip_1.10-6       
##  [52] abind_1.4-5            compiler_4.3.1         withr_2.5.1           
##  [55] mutoss_0.1-13          MASS_7.3-60            rjson_0.2.21          
##  [58] tools_4.3.1            lmtest_0.9-40          httpuv_1.6.11         
##  [61] future.apply_1.11.0    qqconf_1.3.2           goftest_1.2-3         
##  [64] glue_1.6.2             nlme_3.1-163           promises_1.2.1        
##  [67] grid_4.3.1             Rtsne_0.16             cluster_2.1.4         
##  [70] reshape2_1.4.4         generics_0.1.3         gtable_0.3.4          
##  [73] spatstat.data_3.0-1    tidyr_1.3.0            sn_2.1.1              
##  [76] sp_2.0-0               utf8_1.2.3             spatstat.geom_3.2-5   
##  [79] RcppAnnoy_0.0.21       ggrepel_0.9.3          RANN_2.6.1            
##  [82] pillar_1.9.0           stringr_1.5.0          later_1.3.1           
##  [85] splines_4.3.1          lattice_0.21-8         survival_3.5-7        
##  [88] deldir_1.0-9           tidyselect_1.2.0       miniUI_0.1.1.1        
##  [91] pbapply_1.7-2          knitr_1.44             gridExtra_2.3         
##  [94] scattermore_1.2        stats4_4.3.1           xfun_0.40             
##  [97] matrixStats_1.0.0      stringi_1.7.12         lazyeval_0.2.2        
## [100] yaml_2.3.7             evaluate_0.22          codetools_0.2-19      
## [103] tibble_3.2.1           cli_3.6.1              uwot_0.1.16           
## [106] xtable_1.8-4           reticulate_1.32.0      Rdpack_2.5            
## [109] munsell_0.5.0          jquerylib_0.1.4        Rcpp_1.0.11           
## [112] globals_0.16.2         spatstat.random_3.1-6  png_0.1-8             
## [115] parallel_4.3.1         ellipsis_0.3.2         listenv_0.9.0         
## [118] viridisLite_0.4.2      mvtnorm_1.2-3          scales_1.2.1          
## [121] ggridges_0.5.4         leiden_0.4.3           purrr_1.0.2           
## [124] rlang_1.1.1            multcomp_1.4-25        mnormt_2.1.1